Nour-Sadek / Salary-Prediction

This project provided practice with fitting and evaluating linear models with scikit-learn

Salary Prediction

About

Linear regression is one of the simplest yet powerful tools for finding regularities in data and using them for prediction. It is widely applied both in science and practice. In this project, you will learn how to apply scikit-learn library to fit linear models, use them for prediction, compare the models, and select the best one. You will also learn how to carry out testing for certain issues with data.

Learning Outcomes of the Project:

Practice fitting linear models with scikit-learn to predict values on the unknown data. Apply polynomial feature engineering, test your data for multi-collinearity, and evaluate models with the MAPE score.

Learning Outcomes of Each Stage of the Project:

Stage 1 : Fit a simple model with one predictor and evaluate it.

Stage 2 : Use the linear model to handle the polynomial relationship between independent and dependent variables.

Stage 3 : Fit a linear model with many independent variables and compare it with the previous models.

Stage 4 : Check whether the variables have a high correlation and try to improve the model's performance by removing them.

Stage 5 : Get rid of negative predictions and see whether the model performance improves.

General Info

To learn more about this project, please visit HyperSkill Website - Salary Prediction.

This project's difficulty has been labelled as Challenging where this is how HyperSkill describes each of its four available difficulty levels:

• Easy Projects - if you're just starting
• Medium Projects - to build upon the basics
• Hard Projects - to practice all the basic concepts and learn new ones
• Challenging Projects - to perfect your knowledge with challenging tasks

This Repository contains one .py file and one folder:

code.py - Contains the code used to complete the data analysis requirements

Data repository - Contains the data.csv files that contain the data

Project was built using python version 3.11.3

Description of Data Set

It contains the following 9 columns:

• Numerical features

  • rating
  • draft_round
  • age
  • experience
  • bmi

• Non-numerical features

  • team
  • position
  • country

• Target prediction

  • salary

How to Run

Download the files to your local repository and open the project in your choice IDE and run the project. Different models were used to fit the data, evaluated with the Mean Absolute Percentage Error (MAPE) metric, according to each Stage's docstrings. Please read each Stage's docstring to know the requirements.